With the rapid development in the national economy and the acceleration of urbanization, the number of motor vehicles in our country and the volume of road traffic have dramatically increased. The contradiction between increasing of traffic demand and urban road infrastructure has become the principal contradiction in urban traffic, resulting in more and more traffic congestion and traffic jams. Therefore, traffic road information, especially traffic congestion information, has become particularly important. The impact of congestion on road traffic can be minimized by identifying congested road sections.
Currently, the identifying of traffic information mainly involves detection of traffic parameters by microwave radar sensors and estimation of road traffic states by using fuzzy rules and a membership function. However, there are problems as follows in estimating the road traffic states using the above method: 1. Traffic parameters are from single data source, which are detected by using microwave radar sensors only, and errors in the acquired traffic parameters will result in deviation in analysis results for road traffic states. 2. On actual ground roads, traffic lights can cause errors in the analysis results for traffic states for road sections near the traffic lights. 3. The existing fuzzy rule matrix that is used to calculate the road traffic states is too simple and does not vary flexibly with actual situations, which will result in inaccurate analysis results for road traffic states.
So far, no effective solution has been proposed yet for the technical problems, that analysis results for traffic road information are inaccurate due to a single fuzzy rule, in solutions of computing traffic states for a road by using a fuzzy rule described above.